A recent trend is the use of neural-based coreference systems, which can avoid most of those hand-engineered features and rules. One of the early works is the end-to-end neural coreference resolution system proposed by [1]}. There are two key scores in a neural coreference model; one is the mention score, and the other is the antecedent score. The two scores accordingly correspond to the mention detection subtask and mention resolution subtask. It is a common practice to calculate these two scores separately using decoupled models. In [2]}, however, a joint loss function is leveraged to concurrently optimize the performance of both tasks. Additionally, in the same work, a biaffine attention model is used to compute antecedent scores such that better performance can be obtained in comparison to the models using simple feed-forward networks.
